Methods and systems for motion detection in positron emission tomography

ABSTRACT

Methods and systems are provided for medical imaging systems. In one embodiment, a method for a medical imaging system comprises acquiring emission data during a positron emission tomography (PET) scan of a patient, reconstructing a series of live PET images while acquiring the emission data, and tracking motion of the patient during the acquiring based on the series of live PET images. In this way, patient motion during the scan may be identified and compensated for via scan acquisition and/or data processing adjustments, thereby producing a diagnostic PET image with reduced motion artifacts and increased diagnostic quality.

FIELD

Embodiments of the subject matter disclosed herein relate tonon-invasive diagnostic imaging, and more particularly, to positronemission tomography (PET).

BACKGROUND

Positron emission tomography (PET) generates images that represent adistribution of positron-emitting radiotracer within a body of apatient, which may be used to observe metabolic processes in the bodyand diagnose disease. During operation of a PET imaging system, thepatient is initially injected with the radiotracer, which emitspositrons as it decays. Each emitted positron may travel a relativelyshort distance before encountering an electron, at which point anannihilation occurs. When a positron interacts with an electron byannihilation, the entire mass of the positron-electron pair is convertedinto two 511 keV gamma photons (also referred to as 511 keV events). Thephotons are emitted in opposite directions along a line of response(LOR). The annihilation photons are detected by detectors that areplaced on both sides of the LOR, in a configuration such as a detectorring, as coincident events. Thus, during data acquisition, the detectorsdetect the coincident events, which reflect a distribution of theradiotracer in the patient's body. An image thus reconstructed from theacquired image data includes the annihilation photon detectioninformation. Typically, the image is reconstructed upon completion ofthe data acquisition, and it may be unknown if the acquired data isadequate for producing a high quality image until after the image isreconstructed.

BRIEF DESCRIPTION

In one embodiment, a method for a medical imaging system includesacquiring emission data during a positron emission tomography (PET) scanof a patient, reconstructing a series of live PET images while acquiringthe emission data, and tracking motion of the patient during theacquiring based on the series of live PET images. In this way, patientmotion during the PET scan can be accurately detected and compensatedfor, thereby reducing motion artifacts and increasing a diagnosticquality of a resulting PET image.

It should be understood that the brief description above is provided tointroduce in simplified form a selection of concepts that are furtherdescribed in the detailed description. It is not meant to identify keyor essential features of the claimed subject matter, the scope of whichis defined uniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be better understood from reading thefollowing description of non-limiting embodiments, with reference to theattached drawings, wherein below:

FIG. 1 shows a pictorial view of an exemplary multi-modality imagingsystem according to an embodiment of the disclosure.

FIG. 2 shows a block schematic diagram of an exemplary positron emissiontomography (PET) imaging system according to an embodiment of thedisclosure.

FIG. 3 shows a high-level flow chart of an example method for performingPET-computed tomography with real-time PET image reconstruction todetect and respond to patient motion, according to an embodiment of thedisclosure.

FIG. 4 shows a flow chart of an example method for reconstructing PETimages in real-time for patient motion detection, according to anembodiment of the disclosure.

FIG. 5 schematically shows how segments may be selected on a line ofresponse for real-time PET image reconstruction, according to anembodiment of the disclosure.

FIG. 6 schematically shows how projection weights may be determined forreal-time PET image reconstruction, according to an embodiment of thedisclosure.

FIG. 7 shows an example of detecting patient motion during PET using PETimages reconstructed in real-time.

DETAILED DESCRIPTION

The following description relates to various embodiments of medicalimaging systems. In particular, methods and systems are provided forreconstructing positron emission tomography (PET) images in real-timefor patient motion detection. An example of an imaging system that maybe used to acquire images processed in accordance with the presenttechniques is provided in FIG. 1. Herein, the imaging system may be amulti-modality system. In one embodiment, the multi-modality imagingsystem may be a positron emission tomography/computed tomography(PET/CT) imaging system wherein a first modality is a CT imaging systemand a second modality is a PET imaging system (as illustrated in FIGS. 1and 2, for example).

When a patient is scanned using the PET imaging system, events capturedwithin a field-of-view (FOV) of the imaging system may be used toreconstruct functional images of the patient. However, uncompensatedpatient motion during the scan may reduce the quality of resultingimages. For example, image artifacts, blur, and increased noise mayoccur in the PET images due to patient motion during the scan, which maydegrade a diagnostic value of the images. If a technologist operatingthe PET imaging system observes the patient moving, the technologist mayissue instructions to the patient to remain still and extend a durationof the scan. Alternatively, the technologist may repeat the scan.However, because the technologist may not have a clear view of thepatient within the imaging system, the technologist may be unaware thatthe patient is moving, resulting in degraded PET images or other hybridmodality images. In some examples, an inaccurate diagnosis may be madefrom the degraded images. In other examples, the degraded images may notbe used, and a rescan may be requested. The rescan may includere-injecting the patient with radiotracer and repeating an entirety ofthe scan, which increases costs and patient discomfort and reducesimaging system availability.

Therefore, an example method for detecting and tracking patient motionin the PET imaging system is shown in FIG. 3, which utilizes real-timePET images reconstructed using an example fast reconstruction methodshown in FIG. 4. Herein, a list-mode reconstruction is performed onimage space data acquired during very short time frames, allowingmanageable data sizes that can be processed in real-time. For example,time-of-flight (TOF) PET information may be leveraged in the fastreconstruction method to produce image projections, such as shown inFIGS. 5 and 6. Image registration between the real-time PET images fromdifferent time frames may be used for patient motion analysis, anexample of which is shown in FIG. 7. When the patient motion exceeds athreshold that will result in motion-related degradation in a final PETimage used for diagnostics, which is different than the real-time PETimages used for the motion detection, various motion detection responsesmay be employed. For example, a scan time may be selectively extended tocapture additional motion-free data, data acquired during the patientmotion may be discarded, advanced motion correction reconstructiontechniques may be used on the data acquired during the patient motionduring the final image reconstruction, etc. Further, in hybrid imagingmodalities such as the PET/CT system shown in FIG. 1, CT data acquiredprior to the detected patient motion may also be repeated. By addressingpatient motion in real-time during a PET scan, imaging resources may bespent more efficiently, and scan costs may be decreased. Further,patient discomfort may be decreased. Further still, an amount of timebefore an accurate diagnosis is made may be decreased.

Though a PET/CT imaging system is described by way of example, it shouldbe understood that the present techniques may also be useful whenapplied to images acquired using other imaging modalities, such as CT,tomosynthesis, MRI, ultrasound, and so forth. The present discussion ofa PET/CT imaging modality is provided merely as an example of onesuitable imaging modality. In other examples, a PET/MRI imaging systemor other imaging system including a PET imaging modality may be used.

As used herein, the phrase “reconstructing an image” is not intended toexclude embodiments of the present disclosure in which data representingan image is generated but a viewable image is not. Therefore, as usedherein the term “image” broadly refers to both viewable images and datarepresenting a viewable image. However, many embodiments generate, orare configured to generate, at least one viewable image.

Turning now to the figures, a multi-modality imaging system 10 is shownin FIGS. 1 and 2. Multi-modality imaging system 10 may be a suitabletype of imaging system, for example, such as a Positron EmissionTomography (PET) imaging system, a Single Photon Emission ComputedTomography (SPECT) imaging system, a PET/Computed Tomography (CT)imaging system, a PET/ultrasound imaging system, a PET/MagneticResonance Imaging (MRI) system, or any other imaging system capable ofgenerating tomographic images through PET. The various embodiments arenot limited to multi-modality medical imaging systems, but may be usedon a single modality medical imaging system such as a stand-alone PETimaging system or a stand-alone SPECT imaging system, for example.Moreover, the various embodiments are not limited to medical imagingsystems for imaging human subjects, but may include veterinary ornon-medical systems for imaging non-human objects.

Referring first to FIG. 1, the multi-modality imaging system 10 includesa first modality unit 11 and a second modality unit 12. The two modalityunits enable the multi-modality imaging system 10 to scan an object orpatient in a first modality using the first modality unit 11 and in asecond modality using the second modality unit 12. The multi-modalityimaging system 10 allows for multiple scans in different modalities tofacilitate an increased diagnostic capability over single modalitysystems. In the embodiment shown in FIG. 1, multi-modality imagingsystem 10 is a Positron Emission Tomography/Computed Tomography (PET/CT)imaging system 10. In the present example, the first modality unit 11 isa CT imaging system 11, and the second modality unit 12 is a PET imagingsystem 12. The PET/CT system 10 is shown as including a gantry 13 (or afirst gantry portion) included in the CT imaging system 11 and a gantry14 (or a second gantry portion) included in the PET imaging system 12.For example, the CT imaging system 11 may generate anatomical images ofa patient, while the PET imaging system 12 may generate functionalimages corresponding to the distribution of a radiotracer as a marker ofphysiological processes such as metabolism. As discussed above,modalities other than CT and PET may be employed with the multi-modalityimaging system 10.

The gantry 13 includes an x-ray source 15 that projects a beam of x-rayradiation (or x-rays) for use in imaging a patient 21 positioned on amotorized table 24. Specifically, the x-ray source 15 is configured toproject the x-ray radiation beams toward a detector array 18 positionedon the opposite side of the gantry 13. Although FIG. 1 depicts only asingle x-ray source 15, in certain embodiments, multiple x-ray sourcesand detectors may be employed to project a plurality of x-ray radiationbeams for acquiring projection data at different energy levelscorresponding to the patient. In some embodiments, the x-ray source 15may enable dual-energy gemstone spectral imaging (GSI) by rapid peakkilovoltage (kVp) switching. In some embodiments, the x-ray detectoremployed is a photon-counting detector which is capable ofdifferentiating x-ray photons of different energies. In otherembodiments, two sets of x-ray sources and detectors are used togenerate dual-energy projections, with one set at low-kVp and the otherat high-kVp. It should thus be appreciated that the methods describedherein may be implemented with single energy acquisition techniques aswell as dual energy acquisition techniques.

In certain embodiments, the CT imaging system 11 further includes acontroller or processor 25 configured to reconstruct images of a targetvolume of the patient 21 using an iterative or analytic imagereconstruction method. For example, the controller or processor 25 mayuse an analytic image reconstruction approach such as filtered backprojection (FBP) to reconstruct images of a target volume of thepatient. As another example, the controller or processor 25 may use aniterative image reconstruction approach such as advanced statisticaliterative reconstruction (ASIR), conjugate gradient (CG), maximumlikelihood expectation maximization (MLEM), model-based iterativereconstruction (MBIR), and so on to reconstruct images of a targetvolume of the patient 21. As described further herein, in some examplesthe controller or processor 25 may use both an analytic imagereconstruction approach such as FBP in addition to an iterative imagereconstruction approach.

In some CT imaging system configurations, an x-ray source projects acone-shaped x-ray radiation beam which is collimated to lie within anX-Y-Z plane of a Cartesian coordinate system and generally referred toas an “imaging plane.” The x-ray radiation beam passes through an objectbeing imaged, such as the patient or subject. The x-ray radiation beam,after being attenuated by the object, impinges upon an array of detectorelements. The intensity of the attenuated x-ray radiation beam receivedat the detector array is dependent upon the attenuation of a radiationbeam by the object. Each detector element of the array produces aseparate electrical signal that is a measurement of the x-ray beamattenuation at the detector location. The attenuation measurements fromall the detector elements are acquired separately to produce atransmission profile.

In some CT systems, the x-ray source and the detector array are rotatedwith a gantry within the imaging plane and around the object to beimaged such that an angle at which the radiation beam intersects theobject constantly changes. A group of x-ray radiation attenuationmeasurements, e.g., projection data, from the detector array at onegantry angle is referred to as a “view.” A “scan” of the object includesa set of views made at different gantry angles, or view angles, duringone revolution of the x-ray source and detector. It is contemplated thatthe benefits of the methods described herein accrue to medical imagingmodalities other than CT, so as used herein the term “view” is notlimited to the use as described above with respect to projection datafrom one gantry angle. The term “view” is used to mean one dataacquisition whenever there are multiple data acquisitions from differentangles, whether from a CT, PET, or single-photon emission CT (SPECT)acquisition, and/or any other modality including modalities yet to bedeveloped as well as combinations thereof in fused embodiments.

The projection data is processed to reconstruct an image thatcorresponds to a two-dimensional slice taken through the object or, insome examples where the projection data includes multiple views orscans, a three-dimensional rendering of the object. One method forreconstructing an image from a set of projection data is referred to inthe art as the filtered back projection technique. Transmission andemission tomography reconstruction techniques also include statisticaliterative methods such as maximum likelihood expectation maximization(MLEM) and ordered-subsets expectation-reconstruction techniques as wellas iterative reconstruction techniques. This process converts theattenuation measurements from a scan into integers called “CT numbers”or “Hounsfield units,” which are used to control the brightness of acorresponding pixel on a display device.

To reduce the total scan time, a “helical” scan may be performed. Toperform a “helical” scan, the patient is moved while the data for theprescribed number of slices is acquired. Such a system generates asingle helix from a cone beam helical scan. The helix mapped out by thecone beam yields projection data from which images in each prescribedslice may be reconstructed.

In certain embodiments of the multi-modality imaging system 10, thecontroller or processor 25 may be configured to operate both the CTimaging system 11 and the PET imaging system 12. In other embodiments,the CT and the PET imaging systems may each include a dedicatedcontroller that separately controls the CT and the PET imaging systems.

Continuing to FIG. 2, a block schematic diagram of the PET imagingsystem 12 introduced in FIG. 1 is shown. The PET imaging system 12includes a detector ring assembly 40 including a plurality of detectors(or detector crystals) 62. For example, the detector ring assembly 40may be positioned in the gantry 14. Further each of the detectors 62includes one or more crystals (e.g., a scintillation crystals) and oneor more photo sensors. In another example, the detectors 62 may eachinclude one or more avalanche photodiodes, photomultipliers, siliconphotomultipliers, and/or another type of radiation detector. The PETimaging system 12 also includes a controller or processor 44 configuredto control normalization and image reconstruction processes. Controller44 is operatively coupled to an operator workstation 46. In onenon-limiting example, the controller 44 may be an example of thecontroller 25 of FIG. 1. As another example, the controller 44 may beincluded in (e.g., a part of) or communicatively connected to controller25 of FIG. 1, such as via wired or wireless communication. In theexample of FIG. 2, controller 44 includes a data acquisition processor48 and an image reconstruction processor 50, which are interconnectedvia a communication link 52. PET imaging system 12 acquires scan dataand transmits the data to data acquisition processor 48. The scanningoperation is controlled from the operator workstation 46. The dataacquired by the data acquisition processor 48 is reconstructed using theimage reconstruction processor 50.

The detector ring assembly 40 includes a central opening, in which anobject or patient, such as patient 21, may be positioned using, forexample, the motorized table 24 (shown in FIG. 1). The motorized table24 is aligned with a central axis of detector ring assembly 40. Themotorized table 24 moves the patient 21 into the central opening ofdetector ring assembly 40 in response to one or more commands receivedfrom the operator workstation 46. A PET scanner controller 54, alsoreferred to as the PET gantry controller, is provided (e.g., mounted)within PET imaging system 12. The PET scanner controller 54 responds tothe commands received from the operator workstation 46 through thecommunication link 52. Therefore, the scanning operation may becontrolled from the operator workstation 46 through PET scannercontroller 54.

Prior to a PET scan, a radioactive nuclide (e.g., radionuclide), orradiotracer, is delivered to the patient 21. For example, theradionuclide may be fluorine-18, carbon-11, nitrogen-13, oxygen-15, orthe like and may be injected into the patient 21. The radionuclide maybe incorporated into a molecule that is normally metabolized by the bodyor into a molecule that binds to a receptor target, for example. Assuch, the radionuclide accumulates within organs, vessels, or the like.The radionuclide undergoes positron emission decay and thereby emits apositron, which collides with an electron in the surrounding tissue. Thepositron encounters an electron and, when the positron collides with anelectron, both the positron and the electron are annihilated andconverted into a pair of photons, or gamma rays, each having any energyof 511 keV. The two photons are directed in substantially oppositedirections and are each detected when they reach respective detectors 62positioned across from each other on the detector ring assembly 40.Thus, the two detectors 62 detecting the coincident scintillation eventsare positioned substantially 180 degrees from each other. When thephoton collides with the detector, it produces a scintillation event(e.g., flash of light) on the detector crystal. Each photomultipliertube of the respective detector 62 detects the scintillation event andproduces an analog signal that is transmitted on a communication line64. A set of acquisition circuits 66 receive the analog signals from thephotomultiplier tube via the communication line 64. Acquisition circuits66 produce digital signals indicating the three-dimensional (3D)location and total energy of the event. The acquisition circuits 66 alsoproduce an event detection pulse, which indicates the time or moment thescintillation event occurred. These digital signals are transmittedthrough a communication link, for example, a cable, to an event locatorcircuit 68 in the data acquisition processor 48.

The data acquisition processor 48 includes the event locator circuit 68,an acquisition CPU 70, and a coincidence detector 72. The dataacquisition processor 48 periodically samples the signals produced bythe acquisition circuits 66. The acquisition CPU 70 controlscommunications on a back-plane bus 74 and on the communication link 52.The event locator circuit 68 processes the information regarding eachevent and provides a set of digital numbers or values indicative of thedetected event. For example, this information indicates when the eventtook place and the position of the detector 62 that detected the event.An event data packet is communicated to the coincidence detector 72through the back-plane bus 74. The coincidence detector 72 receives theevent data packets from the event locator circuit 68 and determines ifany two of the detected events are in coincidence. Coincidence isdetermined by a number of factors. First, time markers in each eventdata packet must be within a predetermined time period, such as within12.5 nanoseconds of each other, to indicate coincidence. Second, aline-of-response (LOR) 35 formed by a straight line joining the twodetectors that detect the coincidence event should pass through a fieldof view (FOV) 22 in the PET imaging system 12. Events that cannot bepaired are discarded. Coincident event pairs are located and recorded asa coincidence data packet that is communicated through a communicationlink 78 to a sorter/histogrammer 80 in the image reconstructionprocessor 50.

The image reconstruction processor 50 includes the sorter/histogrammer80. During operation, the sorter/histogrammer 80 generates a datastructure known as a histogram. The histogram includes a large number ofcells, where each cell corresponds to a unique pair of detectorscrystals in the PET imaging system 12. Because a PET imaging systemtypically includes thousands of detector crystals, the histogramtypically includes millions of cells. Each cell of the histogram alsostores a count value representing the number of coincidence eventsdetected by the pair of detector crystals for that cell during the scan.At the end of the scan, the data in the histogram is used to reconstructan image of the patient 21. The completed histogram containing all datafrom the scan is commonly referred to as a “result histogram.” The term“histogrammer” generally refers to the components of the controller 44,e.g., processor and memory, which carry out the function of creating thehistogram.

The image reconstruction processor 50 also includes a memory module 82,an image CPU 84, an array processor 86, and a communication bus 88.During operation, the sorter/histogrammer 80 counts all events occurringalong each parallel LORs at an angle φ and forms a projection. Forexample, a line integral along all of the parallel LORs at angle φ and adistance s from a center of the FOV 22 forms a projection p(s, φ). Theprojections for all angles are further organized into a data array 90.The data array 90 may be a sinogram, which is a function of s and φ. Asingle projection fills one row in the sinogram, and the sinogramincludes a superposition of all projections weighted by an average countat each point. Data array 90 is stored in the memory module 82. Thecommunication bus 88 is linked to the communication link 52 through theimage CPU 84. The image CPU 84 controls communication throughcommunication bus 88. The array processor 86 is also connected to thecommunication bus 88. The array processor 86 receives data array 90 asan input and reconstructs images in the form of an image array 92.Resulting image arrays 92 are then stored in memory module 82.

The images stored in the image array 92 are communicated by the imageCPU 84 to the operator workstation 46. The operator workstation 46includes a CPU 94, a display 96, and an input device 98. The CPU 94connects to communication link 52 and receives inputs, e.g., usercommands, from the input device 98. The input device 98 may be, forexample, a keyboard, mouse, a touch-screen panel, and/or a voicerecognition system. Through input device 98 and associated control panelswitches, the operator can control the operation of the PET imagingsystem 12 and the positioning of the patient 21 for a scan. Similarly,the operator can control the display of the resulting image on thedisplay 96 and can perform image-enhancement functions using programsexecuted by the workstation CPU 94.

Further, in some examples, a timing precision for detecting the 511 keVevents may be high enough that the coincidence detector 72 is able tomeasure a time-of-flight (TOF) difference between the two photons. Forexample, when a positron annihilation event occurs closer to a firstdetector crystal than a second detector crystal, one annihilation photonmay reach the first detector crystal before (e.g., nanoseconds orpicoseconds before) the other annihilation photon reaches the seconddetector crystal. The TOF difference may be used to constrain a locationof the positron annihilation event along the LOR, which may increase anaccuracy and quality the image reconstructed by the image reconstructionprocessor 50. A resolution of the TOF difference, or TOF kernel, may bea predetermined value stored in a memory of the controller 44 or may bedetermined based on, for example, a count rate. The same TOF kernel maybe used for analyzing all of the LORs in a dataset, for example.

Note that the various components and processes of controller 44described above are provided as one example of how controller 44 mayobtain, process, and store data generated during operation of PETimaging system 12. In other examples, controller 44 may includedifferent processors and memories with similar or differentfunctionalities to those described above in similar or differentarrangements. In particular, controller 44 may employ parallel ormassively parallel processing. Further, in some embodiments, variousprocessors of controller 44, such as the data acquisition processor 48and the image reconstruction processor 50, may be contained within ashared housing, while in other embodiments, the various processors ofcontroller 44 may be contained within separate housings that are in asame or a different location. Thus, in some examples, the processors ofcontroller 44 may span multiple locations that are communicativelyconnected.

During PET, such as when a PET imaging system (e.g., PET imaging system12 of FIGS. 1 and 2) of a medical imaging facility is operated to imagea patient (e.g., patient 21 FIGS. 1 and 2), the patient may move. Motioncan lead to blurring of the data, increased noise, reduced quantitativeaccuracy, and an introduction of image artifacts. As a result, adiagnostic value of the obtained images may be degraded. A technologistoperating the PET imaging system may be unware that the patient hasmoved and thus may not take steps to address the patient motion. In someexamples, the obtained imaging data may undergo substantial motioncorrection post-imaging, which uses a large amount of computationresources. However, even when the post-imaging motion correction isused, the diagnostic value of the images may still be degraded, whichmay lead to misdiagnosis or a rescan of the patient being ordered. Evenif the patient is immediately available at the medical imaging facility,the previously used radionuclide may be unusable due to isotope decay.As a result, the patient may be reinjected with the radionuclide beforethe scan is repeated. If the patient is not immediately available, thepatient may have to return to the medical imaging facility for therescan. Overall, an amount of time before a diagnostic is made, patientdiscomfort, and an imaging cost all may be increased.

Therefore, FIG. 3 provides an example method 300 for tracking motion ofa patient within an imaging system during a scan based on PET imagesthat are reconstructed in real-time. Method 300 will be described for aPET/CT imaging system, such as imaging system 10 described with respectto FIGS. 1-2, although other PET imaging systems may be used.Instructions for carrying out method 300 and the rest of the methodsincluded herein may be executed by a controller (e.g., controller 25 ofFIG. 1 and/or controller 44 of FIG. 2) based on instructions stored on amemory of the controller and in conjunction with signals received fromsensors of the imaging system, such as the sensors described above withreference to FIGS. 1-2. The controller may employ actuators of theimaging system to adjust the operation of the imaging system accordingto the methods described below.

At 302, method 300 includes receiving radiotracer information from auser (e.g., technologist) of the imaging system. Receiving theradiotracer information from the user includes a type of radiotracerthat is injected into the patient positioned within the imaging system.The radiotracer may be a positron-emitting radionuclide. Somenon-limiting examples of radiotracers include fluorine-18fludeoxyglucose (FDG), carbon-11 choline, nitrogen-13 ammonia, andoxygen-15 water. In some examples, the type of radiotracer injected maydepend on an anatomy of interest that is being imaged. As mentionedabove, the radiotracers injected into the patient may accumulate withinorgans, vessels or the like and begin to decay and emit positrons. Asexplained previously, the positrons annihilate, generating a pair ofgamma rays. In addition to the type of tracer injected, the controllermay receive additional information, such as a time of injection, a doseof radiotracer injected, and a pre-injection delay. In addition toradiotracer information, the controller may receive a weight of thesubject. In one example, the user may enter the weight of the patient.As another example, the controller may additionally receive a selectedimaging protocol, which may be selected by the user or manually input bythe user.

At 304, method 300 includes performing the CT scan. As one example,performing the CT scan may include first performing a CT scout scan. TheCT scout scan may serve as an anatomical reference for the PET/CT scan.In one example, the CT scout scan may be used to define starting andending locations for the CT and PET acquisitions. In some examples, theCT scout scan may be a whole body scan. Once the starting and endinglocations are defined, the method includes acquiring additional CT imagedata within the region defined by the starting and the ending locations.For example, CT image data may be acquired by activating an x-ray source(e.g., x-ray source 15 of FIG. 1) according specified parameters, whichmay be input by the user or specified by the imaging protocol selectedby the user (e.g., a specified kV, mA, attenuation filter position).Further, a gantry (e.g., gantry 13 of FIG. 1) may be rotated achievespecified the angles. Further, during the CT scan, the position of atable of the imaging system (e.g., table 24 of FIG. 1) may be moved suchthat the scan progresses from the start scan location to the stop scanlocation.

At 306, method 300 includes performing the PET scan and acquiringemission data from inside a field of view (FOV) of the imaging system.The PET scan may generate functional images corresponding to dynamicoccurrences such as metabolism. To perform the PET scan and acquire theemission data, detector crystals of the PET imaging system are activatedto detect gamma rays emitted from the patient due to positron emissionand annihilation, and acquisition circuits, event locator circuits, anda coincidence detector may together record coincidence events, aselaborated above with respect to FIG. 2.

At 308, method 300 includes producing real-time PET images during thedata acquisition via a fast reconstruction method. As will be elaboratedbelow with respect to FIGS. 4-6, in some examples, the fastreconstruction method may not use all of the emission data obtained foreach image reconstruction in order to provide the real-time (e.g.,without significant delay) PET images. As such, the real-time PET imagesinclude a series of live images that represent the emission data beingacquired as it is acquired (e.g., at the time of occurrence, with only asub-second or second time delay). For example, some of the data may beskipped and/or discarded by the fast reconstruction method. As anotherexample, the real-time, fast reconstruction method additionally oralternatively may utilize subsetting to reduce processing times.Further, the fast reconstruction method may not employ attenuation orscatter correction, as the images produced may be used to determinepatient position and may not be used for diagnostics, for example.Further still, the fast reconstruction method may not employ motioncorrection.

The fast reconstruction method may use TOF list-mode reconstruction(instead of sinogram-based reconstruction). As such, the controller maynot organize the emission data into a histogram prior to thereconstruction (e.g., sorter/histogrammer 80 of FIG. 2 may not be used).The list-mode data includes a list of all the detected coincidenceevents. Each item in the list identifies the two detector crystalsinvolved, a difference in detected times between the two detectorcrystals (e.g., TOF information, as described above with respect to FIG.2), and an indication of an absolute time that the coincidence wasdetected. The controller may evaluate image values corresponding to theapproximate location of the coincidence based on the LOR between the twodetector crystals and the difference in time that the events weredetected for each item in the list. After a group (e.g., subset oriteration) of events are processed, an update to the image can beapplied. The number of image updates applied may vary from one update toa plurality of updates. Increasing the number of image updates mayprovide a statistically optimal image at the expense of increasingreconstruction time, for example.

The fast reconstruction method may reconstruct image volumes for shorttime frames (e.g., time periods) to produce the real-time PET images.For example, each short time frame may include a pre-defined duration oftime, which may range from milliseconds to seconds. As one non-limitingexample, each short time frame is one second each. In such an example,the pre-defined duration may be used to determine (or define) the eventdata to include for each time frame. As another example, the duration ofeach time frame may vary based on a number of events captured. Forexample, the duration may be adjusted in order to acquire a desirednumber of events (e.g., 4 million). The time frame may extend until thedesired number of new events are detected, and then the subsequent timeframe may commence. In such an example, the number of events may be usedto determine (or define) the event data to include for each time frameas well as the duration of each time frame.

One real-time (e.g., live) PET image may be reconstructed from theemission (e.g., event) data obtained during one time frame, and eachreal-time, live PET image may be referred to herein as an “image frame.”In some examples, the time frames may be contiguous and non-overlapping,while in other examples, the time frames may partially overlap such thata subsequent time frame starts before a preceding time frame ends. Asone example, a first time frame may begin, and the data acquired duringthe first time frame may be reconstructed via the fast reconstructionmethod upon completion of the first time frame (e.g., the pre-determinedduration or the desired number of detected events). While the real-timePET image is produced for the first time frame, data may be collectedfor a second, subsequent time frame. This sequence may be repeatediteratively throughout the scan to produce a series of real-time PETimages.

As one example, the series of real-time PET images may include imageframes reconstructed at pre-determined time points, each time pointseparated by a pre-selected interval (e.g., the pre-defined duration oftime) and each image frame reconstructed from the emission data acquiredduring the immediately preceding interval. In this way, one image framemay be reconstructed every one second, for example. As another example,the series of real-time PET images may include image framesreconstructed after acquiring a pre-determined amount of data. In thisway, one image frame may be reconstructed every n event acquisitions,for example.

As a further example, the image frames may be reconstructed frompartially overlapping sets of data. For example, consecutive imageframes may share a percentage of events in a range from 30-70%, thepercentage of events corresponding to a proportion of total events usedfor reconstructing each consecutive image frame that are shared by theconsecutive image frames. In one non-limiting example, successive imageframes may share a 50% overlap in events. For example, one image framemay be reconstructed from event 1 through N, where N is a predeterminednumber, and the next image frame may be reconstructed from event N/2+1through 3N/2 for a 50% overlap of events between successive frames.However, the data sets used for reconstructing consecutive image framesmay be overlapped in other ways.

Alternatively, if a count rate of events is very high, the number ofevents detected per second may be too great for all of them to be usedin real-time reconstruction. As will be elaborated below with respect toFIG. 4, in such an example, not all of the detected events may be usedin reconstructing the image frame in order to maintain the real-timeperformance of the system. For example, if the count rate of events is10 million per second and the controller is capable of reconstructing 4million per second, then the controller may not use the events after thefirst 4 million, reconstruct an image frame with the first 4 millionevents, and resume collecting events at the start of the next second.However, the controller may continue to store all list events forsubsequent (non real-time) processing. In this way the real-timereconstruction may be maintained regardless of the event count rate.

Because the time frame is short, a number of coincidence events in theemission data obtained during the time frame is relatively small, and byusing the TOF data, only a small portion of the image is considered foreach detected coincidence. As such, the list-mode reconstruction is moreefficient than a sinogram-based reconstruction for the same emissiondata.

Further, the real-time PET images may be reconstructed using anefficient randoms calculation. A random refers to the detection of twophotons that meet criteria for coincidence that are not actually from asame positron annihilation event (e.g., the coincidence is random).Randoms are a source of image degradation in PET and may be compensatedfor during or before image reconstruction. As one example, a singles mapmay show a count rate in individual detectors. Instead of computingrandoms from an expansion of the singles map into a full sinogram, whichuses over 400 million calculations, for the fast reconstruction method,a simple multiplication may be used. For example, a randoms rate (R) fora LOR between detector crystals i and j may be calculated using asingles rate (SR method) according to the equation:R=2S _(i) S _(j)τwhere S_(i) is a singles count of the detector crystal i, S_(j) is asingles count of the detector crystal j, and τ is a timing window fordetecting coincidence. As such, the randoms calculation may be performedusing fewer computational resources. Similar principles may be appliedfor normalization and deadtime. By including randoms and normalizationin the real-time PET image reconstruction, image quality may beincreased, which may aid the motion detection described below.

At 310, method 300 includes evaluating the real-time PET images overtime to track patient motion. For example, registration may be performedon each image frame as it is reconstructed, such as by transforming eachimage frame onto a unified coordinate system. The unified coordinatesystem may provide a reference for comparing the image frames to eachother. In real-time, each newly reconstructed and registered image framemay be compared with the immediately preceding (e.g., previouslyreconstructed) image frame (or a pre-determined number of precedingimage frames) in the series to determine whether or not the patient hasmoved between image frames with respect to the unified coordinatesystem. As such, method 300 may provide real-time motion computationfrom image space.

The registration may be performed using one or more algorithms. In someexamples, the registration algorithm(s) may utilize edge detection todefine boundaries of the patient's anatomy in each image frame and mayfurther use change detection to determine if the boundaries defined bythe edge detection have moved with respect to the unified coordinatesystem over time (e.g., between image frames), as will be elaboratedwith respect to FIG. 7. For example, the edge detection may providerigid registration and may be used when an anatomical feature beingimaged is a rigid body, such as the head. However, the registrationalgorithm(s) may additionally or alternatively utilize othertransformations and analysis methods that enable a comparison of thepatient position between image frames. For example, the controller mayperform non-rigid registration between image frames, such as by using anoptical flow approach. The optical flow approach may create a full 3Dmotion field, showing how each element of a volume has moved as comparedto a reference frame (e.g., an initial time point). Further, theregistration algorithm(s) may determine an absolute magnitude of themotion based on a displacement of the patient (or a displacement of oneor more boundaries) between image frames, for example.

At 312, method 300 includes determining if patient motion is detected.For example, patient motion may be detected when the absolute magnitudeof the motion exceeds a pre-determined motion threshold stored in amemory of the controller. The motion threshold may be calibrated todifferentiate smaller changes in the patient position from largerchanges in the patient position. For example, the smaller changes may bemore accurately compensated for using post-imaging motion correctionalgorithms than the larger changes. As another example, the largerchanges in the patient position may not be accurately corrected viaprocessing or may warrant more complex processing than the smallerchanges in the patient position. For example, when the absolutemagnitude of the motion exceeds the motion threshold, image qualitydegradation may occur. Conversely, patient motion having an absolutemagnitude below the motion threshold may be corrected without degradingthe image quality. Patient motion below the motion threshold may becaused by the patient breathing, for example. The magnitude of themotion may refer to a variation of the patient between image frames,such as a displacement of the patient, a rotation of the patient, and/oranother characteristic that indicates a statistically significantmismatch in the patient position between image frames.

As still another example, the controller may determine a degree ofsimilarity in the overall patient position in two image frames, and thecontroller may indicate patient motion is detected responsive to thedegree of similarity decreasing below a threshold degree of similarity.The threshold degree of similarity may differentiate the smaller changesin the patient position from the larger changes in the patient positiondefined above.

If patient motion is not detected, method 300 proceeds to 330 andincludes reconstructing a CT image. One or more CT images may bereconstructed using, as a non-limiting example, an analyticreconstruction algorithm, such as filtered back projection or aniterative reconstruction algorithm.

At 332, method 300 includes reconstructing a final PET image from theacquired emission data. In particular, the final PET image may becompleted after all of the emission data has been acquired (e.g., afterthe PET scan is complete). As such, the final PET image is not areal-time representation of the acquired emission data (e.g., the finalPET image is a non-live PET image). In some examples, the final PETimage may be reconstructed using emission data spanning an entireduration of the data acquisition. In other examples, such as theexamples that will be noted further herein, a portion of the data may beselectively excluded. In some examples, the final PET image may includeone cross-sectional image of the patient. In other examples, the finalPET image may include multiple cross-sectional images of the patient.Further, because a large number of coincidence events may be used inreconstructing the final PET image relative to the number of coincidenceevents used in reconstructing the real-time PET images, a sinogram-basedreconstruction may be performed. For example, the sinogram-basedreconstruction may more efficiently process the larger data set than thelist-mode reconstruction described above with respect to the real-timePET images. Therefore, the sinogram-based reconstruction will bedescribed below. However, in other examples, list-mode reconstructionmay be used for reconstructing the final PET image.

As explained previously with reference to FIG. 2, an imagereconstruction processor of the PET imaging system may include asorter/histogrammer (such as sorter/histogrammer 80 of FIG. 2). Thesorter/histogrammer includes a histogram of a large number of bins,where each bin corresponds to a unique pair of detector crystals. Eachbin of the histogram also stores a count value representing the numberof coincidence events detected by the pair of detector crystals for thatbin during the scan, which may be organized into a sinogram. After allof the data is acquired and the PET scan is no longer actively beingperformed (e.g., the detector crystals are not actively detectingevents), the data in the histogram is used to reconstruct the final PETimage of the patient. For example, the total number of coincidenceevents detected by the pair of detector crystals will be proportional toan amount of radiotracer within the LOR connecting the pair. The finalPET image may be used by a clinician for diagnostic purposes, whereasthe real-time PET images may not be used for diagnostics.

The controller may use analytic and/or iterative image reconstructionalgorithms. Analytic reconstruction algorithms may provide a directmathematical solution for the reconstructed image, whereas iterativeimage reconstruction algorithms may use multiple mathematical iterationsto arrive at the reconstructed image. Further, the controller may employattenuation and/or scatter correction in reconstructing the final PETimage. For example, various scatter correction methods, such asmodel-based scatter simulation, may be used to estimate scattered eventsduring PET image reconstruction. The model-based scatter simulation usesknowledge of the emission activity and attenuation coefficients and mayinclude both single scatter and multiple scatter estimation. Theemission activity may be estimated by an initial PET imagereconstruction using the acquired PET data within the FOV.

At 334, method 300 includes displaying one or more of the final PETimage and the CT image. The one or more images may be displayed on adisplay screen, such as display 96 of FIG. 2, for example. As mentionedabove, the CT image may define anatomical structures, whereas the PETimage may show dynamic bodily functions, such as metabolism. As alsomentioned above, the radiotracer injected into the subject mayaccumulate in organs. An increased uptake of the radiotracer in an organmay therefore appear as a “hot spot” in the PET image. Abnormal tissue(or tumors) may have increased uptake and hence appear as hot spots inthe PET image. However, normal tissues also uptake different levels ofthe radiotracer. For example, a radiotracer such as FDG is clearedprimarily through the renal system, and thus a normal bladder may have agreatest amount of FDG update. The brain may have a higher FDG uptakethan the adipose tissue, for example. The radiotracer uptake by normaltissues may be physiological, whereas radiotracer uptake by abnormaltissues may be pathological. Thus, the final PET image may providefunctional information to aid in diagnostics.

In some examples, the CT image and the final PET image may besuperimposed (e.g., via co-registration) in order to put the functionalinformation from the PET image into the anatomical context given by theCT image. These views may allow the clinician to correlate and interpretinformation from the two different imaging modalities on one image,which may result in more precise information and more accuratediagnoses. Method 300 may then end.

Returning to 312, if instead patient motion is detected, such as inresponse to the absolute magnitude of the motion exceeding the thresholdmotion, method 300 proceeds to 314 and includes performing a motiondetection response. For example, the controller may indicate thatpatient motion is detected via an internal condition that initiates amotion detection response and/or indicate an alert to the user. Thecontroller may select one or more motion detection responses from aplurality of possible motion detection responses and perform theselected response(s) simultaneously or sequentially. Further, thecontroller may identify the time frame(s) over which the patient motion(or variation) exceeds the threshold ssin order to apply the selectedmotion detection responses accordingly.

Performing the motion detection response optionally includes alertingthe user to the patient motion, as indicated at 316. For example, thecontroller may output an audible and/or visual alert to the user via aworkstation, such as operator workstation 46 of FIG. 2. As one example,the alert may notify the user that patient motion is detected and mayfurther prompt the user to instruct the patient to remain still.

Performing the motion detection response additionally optionallyincludes removing data acquired during the detected patient motion froma data set to be used in the final PET image reconstruction, asindicated at 318. For example, the data acquired while the patientmotion is greater than the threshold may be separated from data acquiredwhile the patient motion is not greater than the threshold, and only thedata acquired while the patient motion is not greater than the thresholdmay be used in reconstructing the final PET image. Removing the dataacquired during the detected patient motion may be performed in additionto or as an alternative to outputting the alert at 316. By segregatingthe data acquired during the detected motion from the data set to beused for the final PET image reconstruction, the data acquired duringthe detected patient motion may be excluded from the final PET image. Assuch, motion artifacts due to the detected patient motion will not bepresent in the final PET image.

Performing the motion detection response optionally includes extendingan acquisition time of the scan to obtain a desired amount ofmotion-free data, as indicated at 320. For example, the desired amountof motion-free data may be a pre-determined count number or dataacquisition duration during which the patient motion remains below thethreshold. The pre-determined count number or data acquisition durationmay be calibrated values stored in a memory of the controller and mayrepresent a minimum amount of desired data for achieving a final PETimage with reduced noise and image artifacts. Extending the acquisitiontime of the scan may be performed in addition to or as an alternative toone or both of outputting the alert at 316 and removing the dataacquired during the detected motion at 318.

Performing the motion detection response optionally includes promptinguse of a motion correction reconstruction technique for data acquiredduring the detected patient motion, as indicated at 322. The motioncorrection technique may not be performed automatically, as it may becomputationally expensive. Therefore, at least in some examples, themotion correction technique may be selectively applied to the dataacquired while the patient motion is greater than the threshold insteadof the entire data set. As another example, the motion correctiontechnique may be selectively applied to the data acquired while thepatient motion is indicated as well as the data acquired after theindicated patient motion. As an illustrative example, the patient motionmay result in the patient moving from a first pose to a second pose.Although the patient may not actively move while in the second pose(e.g., the patient motion remains less than the threshold while in thesecond pose), all of the events acquired while the patient is in thesecond pose may be corrected in order to compensate for differencesbetween the first pose and the second pose. Further, in some examples,the emission data may be segregated into “before motion,” “duringmotion,” and “after motion” data sets, which may be at least partiallyprocessed separately from one another in order to accurately correct forthe patient motion while increasing computational efficiency. Promptingthe use of the motion correction reconstruction technique may beperformed in addition to or as an alternative to any or all ofoutputting the alert at 316, removing the data acquired during thedetected motion at 318, and extending the acquisition time of the scanat 320. By performing the motion correction technique, the data acquiredduring the detected patient motion may still be used in the final PETimage reconstruction without introducing blur and motion artifacts, forexample.

Performing the motion detection response optionally includes displayinga real-time motion plot, as indicated at 324. For example, the real-timemotion plot may show patient displacement within the FOV over time inthe three spatial directions, which may be displayed to the user via thedisplay screen. Because the user may be unable to see the patient motionwithin the gantry, the real-time motion plot may allow the user to moreeasily monitor the movement of the patient during the scan. Displayingthe real-time motion plot may be performed in addition to or as analternative to any or all of outputting the alert at 316, removing thedata acquired during the detected motion at 318, extending theacquisition time of the scan at 320, and prompting the use of the motioncorrection reconstruction technique at 322.

Performing the motion detection response optionally includes repeatingthe CT scan of the patient after motion has been detected, as indicatedat 326. For example, new CT image data may be acquired by activating thex-ray source while the gantry is rotated to achieve the angles specifiedby the imaging protocol. The new CT image data may replace the earlierCT image data, which may be discarded or temporarily stored separatedfrom the new CT image data until the CT image is reconstructed.Repeating the CT scan may be performed in addition to or as analternative to any or all of outputting the alert at 316, removing thedata acquired during the detected motion at 318, extending theacquisition time of the scan at 320, prompting the use of the motioncorrection reconstruction technique at 322, and displaying the real-timemotion plot at 324.

Although motion detection during the PET scan may not directly correlateto patient motion during the CT scan, repeating the CT scan may increasea likelihood that motion-free and correctly registered CT scan data isobtained. However, in other hybrid imaging modalities in which data isacquired for the two modalities simultaneously, such as in PET/MRI, thedetected motion affects both imaging modalities. As such, the method at326 may include selectively repeating MR scan portions performed duringthe detected patient motion (and not repeating the entire MR scan, forexample).

Performing the motion detection response optionally includes adaptingboth the PET and CT data acquisition based on the detected patientmotion, as indicated at 328. For example, the PET and CT dataacquisition may be extended in order to capture additional motion-freedata. Extending the PET and CT data acquisition when patient motion isdetected (and not when patient motion is not detected) may enable theimaging system resources to be more efficiently used while alsoincreasing the image quality of both the final PET image and the CTimage. Adapting both the PET and the CT data acquisition based on thedetected patient motion may be performed in addition to or as analternative to any or all of outputting the alert at 316, removing thedata acquired during the detected motion at 318, extending theacquisition time of the scan at 320, prompting the use of the motioncorrection reconstruction technique at 322, displaying the real-timemotion plot at 324, and repeating the CT scan at 326.

Method 300 proceeds to 330 and includes reconstructing the CT image, asdescribed above. Thus, the CT image may be reconstructed afteradditional data is acquired and/or compensations are performed for thedetected patient motion. As a result, the CT image may have fewer motionartifacts, and the image quality may be increased.

The final PET image reconstructed at 332 may also have increased imagequality compared with not performing the selected motion detectionresponse(s). For example, the data acquired during the patient motionmay not be used for reconstructing the final PET image (e.g., whenselected at 318), thereby reducing blur and noise in the final PET imagewhile increasing a quantitative accuracy of the final PET image. Asanother example, the motion correction reconstruction technique may beapplied to the data acquired during the patient motion (e.g., whenselected at 322), and the corrected data acquired during the patientmotion may be used for reconstructing the final PET image withoutintroducing blur and noise.

By tracking patient motion in real-time (or near real-time) using thefast reconstruction method, various adjustments and adaptations to theimaging protocol may be made in real-time in order to compensate for thepatient motion and/or exclude data obtained during the patient motionwithout negatively affecting image quality or scanner resources. In thisway, PET images may be obtained with fewer motion artifacts, increasinga quality of the PET images and a diagnostic accuracy of the PET images.Further, CT images also may be reconstructed from data obtained whilethe patient is not moving, increasing the quality and accuracy of the CTimages. Overall, an occurrence of patient rescans may be decreased whilea quantitative accuracy of the final PET images may be increased,reducing imaging costs and a time to diagnosis.

Continuing to FIG. 4, an example method 400 for the fast reconstructionmethod introduced in FIG. 3 is shown. For example, method 400 may beperformed by the controller as a part of method 300 of FIG. 3 (e.g., at308) in real-time during PET data acquisition. At least parts of method400 may be performed as one iteration of an image update for a singleimage frame, and multiple iterations may be performed in parallel forthe single image frame, at least in some examples.

At 402, method 400 includes determining events to use for each subset ofimaging data. For each short time frame described above at 308 forreconstructing real-time PET images (e.g., image frames), subsetting maybe used to reach convergence more quickly. Instead of iterating over allevents acquired during the time frame for every update of the imageframe, every nth event may be used (where n is an integer), and n passesmay be made over the data using a different subset of the events foreach pass. Each pass may comprise one update, for example.

Therefore, in some examples, the controller may determine the value of nfor determining the events to use for each subset. The value of n may bedetermined based on a data density and matrix size of the data set, forexample, in order to maintain at least a lower threshold number ofevents in each subset. The lower threshold number of events may be apre-determined value stored in memory that corresponds to an eventnumber below which there is not enough data to produce an accurate imageupdate. The controller may input the data density and the matrix sizeinto a look-up table or algorithm, which may output the value of n forthe given data set. The controller may then determine the events to usefor each subset of the imaging data based on the determined value of n,such as by creating a look-up table of events to use for a given subset.As an illustrative example, when n is 4, a first iteration may use event1, 5, 9, 13, etc.; a second iteration may use events 2, 6, 10, 14, etc.;a third iteration may use events 3, 7, 11, 15, etc.; and a fourthiteration may use events 4, 8, 12, 16, etc. In this way, a moreconverged image may be reached more quickly, with four updates to theimage after processing each event just once, as will be furtherelaborated below. However, in some examples, as the time framedecreases, the number of events is smaller, and the data set cannot bebroken into subsets (e.g., n is equal to 1).

In some examples, when the number of events captured in each time frameis higher than an upper threshold number of events, the controller maychoose to skip events in order to reduce processing time. The upperthreshold number of events may be a pre-calibrated value stored inmemory that corresponds to an event number above which an anticipatedprocessing time is greater than a pre-determined threshold duration forreal-time processing. In some examples, the controller may adjust (e.g.,increase) the value of n determined above and then only process afraction of the subsets. Continuing the above illustrative example wheren is 4, n may be increased to 5, and only 4 of the 5 subsets may beprocessed, for example, resulting in a 20% decrease in the processingtime. As another example, additionally or alternatively, the totalnumber of events in the data set may be decreased prior to determiningthe events for each subset. As an illustrative example, only the first 4million events may be selected from 5 million total events, which mayimpart a slight time bias on the result.

In some examples, the controller may update the lower threshold numberof events and/or the upper threshold number of events by measuring anactual time taken to process a data set of a given size. In this way,the controller may refine the fast reconstruction method as it isimplemented in order to ensure generation of real-time PET images thatprovide accurate and timely patient motion detection.

At 404, method 400 includes assigning each event to a subset index. Forexample, the events in a single subset may be divided into groups orblocks of events, which may be stored in contiguous memory. Thecontiguous memory may undergo parallel processing, with each parallelthread assigned to process one block of events. For example, eachparallel thread may access only one block of the given subset of theimaging data stored therein, reducing an amount of data processed byeach thread. Further, at a start of each image update (e.g., eachiteration), each thread gets its own copy of an update matrix so thatthe threads do not interfere with each other. When the threads have allcompleted their individual processing tasks, which are outlined below(e.g., from 406 to 410), their matrices are joined together before beingapplied to a single image update associated with that subset, which maybe stored in the contiguous memory or in non-contiguous memory.

At 406, method 400 includes, for each event, determining a startposition and a stop position of a projection within a line of response(LOR) for a short time-of-fight (TOF) kernel. Using the parallelprocessing described above, the controller may find the start positionand the stop position for each coincident event in the given subset ofdata that travels through the imaging FOV, for example. As mentionedabove with respect to FIG. 2, the TOF kernel may be standardized acrossthe dataset so that the same TOF kernel is used for each LOR. Thus, theTOF kernel may define a shortest valid segment to project for a givenLOR. As another example, using segments longer than the TOF kernel mayresult in increased processing times. Therefore, the controller maydetermine the start position and the stop position of each projectionusing the position of the TOF kernel on the LOR, which may further bedetermined from a timing difference between detecting each photon of anevent.

Turning briefly to FIG. 5, a diagram 500 shows an illustrative exampleof determining how the start position and the stop position of theprojection for different lines of response. FIG. 5 shows a detector ringassembly 540 of a PET imaging system, including a plurality of detectors562. For example, the detector ring assembly 540 may be the detectorring assembly 40 of FIG. 2, and the plurality of detectors 562 may bethe plurality of detectors 62 of FIG. 2. Diagram 500 also shows a PETimaging field of view (FOV) 522. The FOV 522 may include a patientpositioned within the detector ring assembly 540, for example.

Three lines of response are shown in diagram 500: a first line A, asecond line B, and a third line C. Line A does not cross the FOV 522.Therefore, no projection is determined for line A. Line B and line Ceach cross the FOV 522, and the detected events for each line aredetermined to be coincident (e.g., as determined by coincidence detector72 of FIG. 2). Four points are computed for line B, including b1, b2,b3, and b4. The point b1 is where line B enters the FOV 522, the pointb2 is where a TOF kernel starts for line B, the point b3 is where theTOF kernel ends for line B, and the point b4 is where line B exits FOV522. The point b2 is determined as the start position and the point b3is determined as the stop position for the projection for line B inorder to encompass the TOF kernel for line B.

Four points are also computed for line C, including c1, c2, c3, and c4.The point c1 is where a TOF kernel starts for line C, the point c2 iswhere line C enters the FOV 522, the point c3 is where the TOF kernelends for line C, and the point c4 is where line C exits the FOV 522. Thepoint c2 is determined as the start position and the point c3 isdetermined as the stop position for the projection for line C in orderto encompass the TOF kernel for line C.

Returning to FIG. 4, at 408, method 400 includes, for each event,computing projection coefficients (weights) based on a path length and aTOF kernel height for each voxel traversed by the TOF kernel. Forexample, the controller may compute a fast Siddon projection based onvoxel identification and weight (e.g., derived from the path length andthe TOF kernel height) in a sparse matrix multiplication. The pathlength and the TOF kernel height at the center of each segment may bedetermined for each voxel boundary crossing, for example. The controllermay compute the projection for each event in the data acquired duringthe given time frame, for example.

Turning briefly to FIG. 6, a diagram 600 shows an illustrative exampleof determining the path length and the TOF kernel height for anefficient projector. Although diagram 600 shows a two-dimensionalrepresentation, note that the actual calculation may be in threedimensions. Diagram 600 shows a plurality of voxels 602, a directionvector n, which may be a portion of a LOR, for example, a start position606, and a stop position 608. The start position 606 and the stopposition 608 correspond to the start position and the stop position ofthe TOF kernel described above with respect to FIGS. 4 and 5 (e.g., asdetermined at 406). Thus, the portion of the direction vector n betweenthe start position 606 and the stop position 608 corresponds to part ofthe TOF kernel that will be used in the projection of an event.

A factor α is defined as a distance along the direction vector n from acenter of the TOF kernel. Thus, the center of the TOF kernel is at α=0.For each step in x (e.g., dx, shown going from left to right on diagram600), α changes by dx/n[0]. A “next α” look-up may be kept for crossingvoxel boundaries. A smallest step in α that hits a voxel boundary isused for determining path lengths and TOF kernel heights. For example,going from the start position 606 to the stop position 608 along thedirection vector n, there are six voxel boundary crossings, resulting inseven segments L1, L2, L3, L4, L5, L6, and L7 of varying lengths. Thelength of each segment may be determined based on the change in α. Eachsegment has a corresponding TOF kernel height. As shown, segment L1 hasa TOF kernel height H1, segment L2 has a TOF kernel height H2, segmentL3 has a TOF kernel height H3, segment L4 has a TOF kernel height H4,segment L5 has a TOF kernel height H5, segment L6 has a TOF kernelheight H6, and segment L7 has a TOF kernel height H7. The length of eachsegment multiplied by the corresponding TOF kernel height may be storedalong with the voxel ID for each voxel traversed by the direction vectorn between the start position 606 and the stop position 608. A projection610 may then be determined from a sparse matrix multiplication of thestored values.

Returning to FIG. 4, at 410, method 400 includes, for each event,performing a forward projection based on the projection coefficients(e.g., determined above at 408), applying correction(s), andbackprojecting based on the projection coefficients. As an example,within each subset, each event may be forward projected, adjusted forcorrections, and then backprojected. The forward and back projection maybe performed by accessing the sparse matrix elements, for example, withone image reconstructed per processing thread to reduce or avoid memorylocks. Further, as mentioned above with respect to 308 of FIG. 3, thereconstruction algorithm may not employ attenuation or scattercorrection and may use an efficient randoms calculation.

At 412, method 400 includes combining the backprojection of each eventwithin a given subset. For example, the backprojections from all eventswithin the subset may be summed together, and this may be used togenerate an image update. Thus, the data from each subset may provideone image update.

At 414, method 400 includes iteratively updating the image based on eachsubset. For example, the controller may use an iterative reconstructionalgorithm that updates an image estimate until a desired solution isachieved. The desired solution may be a maximum likelihood solution, forexample. As mentioned above, each subset may provide one image update.An initial blank (e.g., uniform) image may be updated using a firstimage update generated from a first subset of the emission data acquiredduring the given time frame (e.g., a first iteration). The resultingupdated image may be further updated using a second image updategenerated from a second subset of the emission data (e.g., a seconditeration). This process may be repeated with the image update from eachsubset of the events, enabling the image estimate to converge (e.g.,reach the desired solution). The final updated image comprises thereal-time PET image for one time frame. Method 400 may then return. Forexample, method 400 may be repeated for each subsequent time frame sothat a subsequent image frame may be produced for motion detection viathe method of FIG. 3.

Next, FIG. 7 shows an example implementation 700 of patient motiondetection during PET based on PET image frames reconstructed inreal-time. For example, a controller (e.g., controller 25 of FIG. 1and/or controller 44 of FIG. 2) may detect the patient motion during PETacquisition according to the method of FIG. 3 using real-time PET imagesreconstructed using TOF list-mode reconstruction without attenuation andscatter correction, such as according to the fast reconstruction methodof FIG. 4. Note that implementation 700 is one illustrative example ofhow the PET image frames may be analyzed to determine patient motion,and in other examples, the controller may perform other analyses.

Implementation 700 shows a series of PET image frames reconstructed fromemission data acquired over time, including a first image frame 702, asecond image frame 704, and a third image frame 706. Each of the firstimage frame 702, the second image frame 704, and the third image frame706 are reconstructed from data acquired over a short duration (e.g., 1second each), as described above with respect to FIGS. 3 and 4, and eachshow a side profile of a patient's skull. The first image frame 702 isthe earliest frame, and the third image frame 706 is the latest timeframe, as shown by a time axis 701. In particular, the first image frame702 is the first image frame in the series (e.g., frame 1), and thesecond image frame 704 is the next image frame in the series (e.g.,frame 2, immediately following frame 1 without any other image frames inbetween). The third image frame 706 is some number of image framesfollowing the second image frame 704 (e.g., frame n).

In the example implementation 700, upon reconstructing each image frame,the controller performs registration to transform the image frame onto aunified coordinate system, shown as a grid, and uses edge detection todefine boundaries of the patient's anatomy in each image frame. FIG. 7shows a first boundary line 708 for the top of the patient's skull inthe first image fame 702, a second boundary line 710 for the top of thepatient's skull in the second image frame 704, and a third boundary line712 for the top of the patient's skull in the third image frame 706,although other boundary lines additionally or alternatively may be usedfor determining and tracking patient motion between image frames.Further, for comparison, the first boundary line 708 is shown as adashed overlay on the second image frame 704, and both the firstboundary line 708 and the second boundary line 710 are shown as dashedoverlays on the third image frame 706.

Once at least two image frames are acquired (e.g., the first image frame702 and the second image frame 704 in the example shown in FIG. 7), thecontroller compares positions of corresponding boundary lines betweenthe two image frames to determine a displacement between them. In theexample shown, the patient's skull has shifted between the first imageframe 702 and the second image frame 704, as illustrated by the positionof the second boundary line 710 relative to the first boundary line 708on the second image frame 704. The controller may directly determine amagnitude of the displacement based on the difference in the position ofthe second boundary line 710 relative to the first boundary line 708 andindicate patient motion responsive to the magnitude exceeding athreshold, as described above at 312 of FIG. 3.

In some examples, the boundaries of non-consecutive image frames in theseries may also be compared to track patient motion over time. In theexample shown, the third boundary line 712 is compared to both thesecond boundary line 710 and the first boundary line 708 even though oneor more image frames are acquired between the second image frame 704 andthe third image frame 706. The position of the third boundary line 712relative to the second boundary line 710 shows that the patient's skullhas shifted between the second image frame 704 and the third image frame706 in the same direction as between the first image frame 702 and thesecond image frame 704 (e.g., in the downward direction with respect tothe page), although it may be understood that image frames following thesecond image frame 704 and preceding the third image frame 706 may showpatient motion in other directions. For example, the patient may move inthe upward direction (with respect to the page) before moving downwardagain and reaching the position shown in the third frame 706.

The controller may determine a displacement between the third boundaryline 712 and one or both of the first boundary line 708 and the secondboundary line 710. The magnitude of the displacement between the thirdboundary line 712 and the second boundary line 710 is smaller than themagnitude of the displacement between the first boundary line 708 andthe second boundary line 710. Further, the displacement between thefirst boundary line 708 and the third boundary line 712 is the greatest.As one example, the series shown in example implementation may indicatethat the patient is consistently moving in the downward directiondepending on any displacement shown in intervening image frames betweenthe second image frame 704 and the third image frame 706. In someexamples, the controller may generate a plot of the displacement (orchange in boundary position) between consecutive image frames to trackthe patient motion over time in both magnitude and direction. Further,in some examples, the plot may be displayed to an operator in real-time(e.g., at 324 of FIG. 3).

In this way, patient motion may be tracked in real-time in order toidentify emission data obtained during periods of movement (e.g., when amagnitude of the patient motion exceeds a threshold). As a result,various adjustments and adaptations to an imaging protocol may be madein real-time in order to compensate for the patient motion and/orexclude data obtained during the patient motion without negativelyaffecting image quality or scanner resources. By tracking the patientmotion in real-time, diagnostic PET images may be generated with fewermotion artifacts, increasing a quality of the diagnostic PET images anda diagnostic accuracy of the diagnostic PET images. Further, by trackingthe patient motion using PET image frames reconstructed using a fastreconstruction method that uses list-mode TOF data and does not employscatter correction, attenuation correction, and motion correction, thePET image frames may be reconstructed live (e.g., as the data isacquired) while avoiding memory locks and reducing processing time.Overall, an occurrence of patient rescans may be decreased while aquantitative accuracy of the final PET images may be increased, reducingimaging costs and a time to diagnosis.

The technical effect of computing a series of PET image frames fromimage space using data acquired over a short time duration is that theseries of PET image frames may be reconstructed in real-time, enablinglive tracking of patient motion.

An example provides a method for a medical imaging system, comprisingacquiring emission data during a positron emission tomography (PET) scanof a patient; reconstructing a series of live PET images while acquiringthe emission data; and tracking motion of the patient during theacquiring based on the series of live PET images.

In an example, reconstructing the series of live PET images whileacquiring the emission data includes performing a list-modereconstruction in real-time.

In examples, each live PET image in the series of live PET images isreconstructed from emission data acquired during a defined timeduration. In some examples, reconstructing the series of live PET imageswhile acquiring the emission data includes, for each live PET image inthe series of live PET images: dividing the emission data acquiredduring the defined time duration into subsets; reconstructing an imageiteration from each subset of the emission data; and combining the imageiteration from every subset to produce the live PET image. In oneexample, reconstructing the image iteration from each subset of theemission data includes assigning the emission data within a given subsetto a plurality of groups and processing each group in parallel. Inanother example, reconstructing the image iteration from each subset ofthe emission data includes determining a start position and a stopposition for a TOF kernel within each line of response (LOR) in thegiven subset of the emission data; computing projection coefficientsbased on a path length and kernel height for each voxel traversed by theTOF kernel; and reconstructing the image iteration by forward and backprojecting using the computed projection coefficients.

In examples, tracking the motion of the patient during the PET scanbased on the series of live PET images includes performing imageregistration on each live PET image in the series of live PET images;determining variation of the patient between time points in the seriesof live PET images; and responsive to the variation exceeding athreshold, performing a motion response. As one example, performing themotion response includes at least one of outputting an alert, displayinga plot of the motion of the patient, extending an acquisition time foracquiring the emission data, segregating emission data acquired whilethe displacement is greater than the threshold from emission dataacquired while the displacement is less than the threshold, andprompting use of a motion correction reconstruction technique for theemission data acquired while the displacement is greater than thethreshold.

The method may further comprise reconstructing a non-live PET imageafter acquiring the emission data, wherein reconstructing the non-livePET image includes performing scatter correction and attenuationcorrection, and reconstructing the series of live PET images includesnot performing the scatter correction and attenuation correction.

The method may further comprise reconstructing a non-live PET imageafter acquiring the emission data, wherein reconstructing the non-livePET image includes performing motion correction, and reconstructing theseries of live PET images includes not performing the motion correction.

An example method for positron emission tomography (PET), comprisesreconstructing image frames using emission data acquired in real-timewhile performing an emission scan of a patient; indicating patientmotion in response to a displacement of the patient between image framesexceeding a threshold; and in response to the patient motion, performingone or more motion detection responses.

In an example, performing one or more motion detection responsesincludes: segregating emission data acquired during the indicatedpatient motion; and not using the segregated emission data duringreconstruction of a final PET image of the patient.

In an example, performing one or more motion detection responsesincludes performing motion correction on only emission data acquiredduring and after the indicated patient motion during reconstruction of afinal PET image of the patient.

In an example, performing one or more motion detection responsesincludes extending an acquisition time for performing the emission scanof the patient.

In examples, each image frame is reconstructed from emission dataacquired during a determined time period of the emission scan, andreconstructing the image frames using the emission data acquired inreal-time while performing the emission scan of the patient includes,for each image frame: determining projections from the emission dataacquired during the determined time period as soon as the determinedtime period is complete; and reconstructing the image frame from thedetermined projections.

An example system comprises a detector array configured to acquireemission data during a scan of a subject; and a processor operationallycoupled to the detector array storing executable instructions innon-transitory memory that, when executed, cause the processor to: trackmotion of the subject in real-time during the scan based on the acquiredemission data; and adjust parameters of the scan responsive to themotion of the subject exceeding a threshold. In examples, to track themotion of the subject in real-time during the scan based on the acquiredemission data, the processor includes additional executable instructionsin non-transitory memory that, when executed, cause the processor to:reconstruct images of the subject at pre-determined time points, eachtime point separated by a defined interval and each image reconstructedfrom emission data acquired during the immediately preceding interval;and compare a current image of the subject with a previous image todetermine a magnitude of the motion of the subject between the previousimage and the current image. In one example, to reconstruct the imagesof the subject, the processor includes additional executableinstructions in non-transitory memory that, when executed, cause theprocessor to: perform parallel processing of subsetted emission datadetermined from the emission data acquired during the immediatelypreceding interval; determine projections from the subsetted emissiondata; and reconstruct the current image from the determined projections.

In an example, to adjust the parameters of the scan responsive to themotion of the subject exceeding the threshold, the processor includesadditional executable instructions in non-transitory memory that, whenexecuted, cause the processor to: extend a duration of the scan; andoutput a motion detection alert.

In an example, to adjust the parameters of the scan responsive to themotion of the subject exceeding the threshold, the processor includesadditional executable instructions in non-transitory memory that, whenexecuted, cause the processor to: separate emission data acquired whilethe motion of the subject exceeds the threshold from emission dataacquired while the motion of the subject does not exceed the threshold;and reconstruct a diagnostic image of the subject using only theemission data acquired while the motion of the subject does not exceedthe threshold.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralof said elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features. Moreover, unlessexplicitly stated to the contrary, embodiments “comprising,”“including,” or “having” an element or a plurality of elements having aparticular property may include additional such elements not having thatproperty. The terms “including” and “in which” are used as theplain-language equivalents of the respective terms “comprising” and“wherein.” Moreover, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements or a particular positional order on their objects.

This written description uses examples to disclose the invention,including the best mode, and also to enable a person of ordinary skillin the relevant art to practice the invention, including making andusing any devices or systems and performing any incorporated methods.The patentable scope of the invention is defined by the claims, and mayinclude other examples that occur to those of ordinary skill in the art.Such other examples are intended to be within the scope of the claims ifthey have structural elements that do not differ from the literallanguage of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

The invention claimed is:
 1. A method for a medical imaging system,comprising: acquiring emission data during a positron emissiontomography (PET) scan of a patient; reconstructing a series of live PETimages while acquiring the emission data without performing scattercorrection and attenuation correction; tracking motion of the patientduring the acquiring based on the series of live PET images; andreconstructing a non-live PET image after acquiring the emission data,including performing the scatter correction and the attenuationcorrection.
 2. The method of claim 1, wherein reconstructing the seriesof live PET images while acquiring the emission data includes performinga list-mode reconstruction in real-time.
 3. The method of claim 1,wherein each live PET image in the series of live PET images isreconstructed from emission data acquired during a defined timeduration.
 4. The method of claim 3, wherein reconstructing the series oflive PET images while acquiring the emission data includes, for eachlive PET image in the series of live PET images: dividing the emissiondata acquired during the defined time duration into subsets;reconstructing an image iteration from each subset of the emission data;and combining the image iteration from every subset to produce the livePET image.
 5. The method of claim 4, wherein reconstructing the imageiteration from each subset of the emission data includes assigning theemission data within a given subset to a plurality of groups andprocessing each group in parallel.
 6. The method of claim 4, whereinreconstructing the image iteration from each subset of the emission dataincludes, for each subset of the emission data: determining a startposition and a stop position for a TOF kernel within each line ofresponse (LOR); computing projection coefficients based on a path lengthand kernel height for each voxel traversed by the TOF kernel; andreconstructing the image iteration by forward and back projecting usingthe computed projection coefficients.
 7. The method of claim 1, whereintracking the motion of the patient during the PET scan based on theseries of live PET images includes: performing image registration oneach live PET image in the series of live PET images; determining avariation of the patient between time points in the series of live PETimages; and responsive to the variation exceeding a threshold,performing a motion response.
 8. The method of claim 7, whereinperforming the motion response includes at least one of outputting analert, displaying a plot of the motion of the patient, extending anacquisition time for acquiring the emission data, segregating emissiondata acquired while the variation is greater than the threshold fromemission data acquired while the variation is less than the threshold,and prompting use of a motion correction reconstruction technique forthe emission data acquired while the variation is greater than thethreshold.
 9. The method of claim 1, wherein reconstructing the non-livePET image includes performing motion correction, and whereinreconstructing the series of live PET images includes not performing themotion correction.
 10. A method for positron emission tomography (PET),comprising: acquiring emission data while performing an emission scan ofa patient; reconstructing real-time image frames during the emissionscan using the emission data as the emission data is acquired;determining a displacement of the patient between consecutive imageframes of the real-time image frames; indicating patient motion inresponse to the displacement of the patient between the consecutiveimage frames exceeding a threshold; and not using the emission dataacquired while the displacement of the patient between the consecutiveimage frames exceeds the threshold during reconstruction of a final PETimage of the patient after the emission scan.
 11. The method of claim10, further comprising performing motion correction on only emissiondata acquired after, and not before, the indicated patient motion duringthe reconstruction of the final PET image of the patient.
 12. The methodof claim 10, further comprising extending an acquisition time forperforming the emission scan of the patient in response to the indicatedpatient motion.
 13. The method of claim 10, wherein each real-time imageframe is reconstructed from emission data acquired during a determinedtime period of the emission scan, and reconstructing the real-time imageframes during the emission scan using the emission data as the emissiondata is acquired includes, for each real-time image frame: determiningprojections from the emission data acquired during the determined timeperiod as soon as the determined time period is complete; andreconstructing the real-time image frame from the determinedprojections.
 14. A system, comprising: a detector array configured toacquire emission data during a scan of a subject; and a processoroperationally coupled to the detector array storing executableinstructions in non-transitory memory that, when executed, cause theprocessor to: track motion of the subject in real-time during the scanbased on the acquired emission data by reconstructing images of thesubject at pre-determined time points without scatter correction andwithout attenuation correction; adjust parameters of the scan responsiveto the motion of the subject exceeding a threshold; and after the scanof the subject, reconstruct a diagnostic image of the subject with thescatter correction and the attenuation correction.
 15. The system ofclaim 14, wherein to track the motion of the subject in real-time duringthe scan based on the acquired emission data by reconstructing theimages of the subject at the pre-determined time points without thescatter correction and the attenuation correction, the processorincludes additional executable instructions in non-transitory memorythat, when executed, cause the processor to: reconstruct the images ofthe subject using emission data acquired during a defined intervalimmediately preceding a current time point of the pre-determined timepoints; and compare a current image of the subject with a previous imageto determine a magnitude of the motion of the subject between theprevious image and the current image.
 16. The system of claim 15,wherein to reconstruct the images of the subject, the processor includesadditional executable instructions in non-transitory memory that, whenexecuted, cause the processor to: perform parallel processing ofsubsetted emission data determined from the emission data acquiredduring the defined interval; determine projections from the subsettedemission data; and reconstruct the current image from the determinedprojections.
 17. The system of claim 14, wherein to adjust theparameters of the scan responsive to the motion of the subject exceedingthe threshold, the processor includes additional executable instructionsin non-transitory memory that, when executed, cause the processor to:extend a duration of the scan; and output a motion detection alert. 18.The system of claim 14, wherein to adjust the parameters of the scanresponsive to the motion of the subject exceeding the threshold, theprocessor includes additional executable instructions in non-transitorymemory that, when executed, cause the processor to: reconstruct thediagnostic image of the subject using only the emission data acquiredwhile the motion of the subject does not exceed the threshold.